An efficient learning technique to predict link quality in WSNReport as inadecuate

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1 PRISM - Parallélisme, Réseaux, Systèmes, Modélisation 2 HIPERCOM2 - High PERformance COMmunications Inria Paris-Rocquencourt

Abstract : In this paper, we apply learning techniques to predict link quality evolution in a wireless sensor network WSN and take advantage of wireless links with the best possible quality to improve the packet delivery rate. We model this problem as a forecaster prediction game based on the advice of several experts. The forecaster learns on-line how to adjust its prediction to better fit the environment metric values. Simulations using traces collected in a real WSN show the improvement of the prediction when the experts use the SES prediction strategy, whereas the forecaster uses the EWA learning strategy.

Keywords : forecaster link quality wireless sensor networks cumulated loss Machine learning expert prediction

Author: Dana Marinca - Pascale Minet - Nesrine Ben Hassine -



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